Recurrent Neural Networks (RNNs) are designed to selectively recognize patterns within input data which are organized as time series by inferring dependencies among subsequent data timesteps. RNNs are able to accomplish this with the use of a sort of internal memory which gets updated at each timestep being processed in order to keep track of meaningful information over the entire sequence of input. Nevertheless, the fact that RNNs repeatedly update their internal memory makes these networks suffer from the well known vanishing/exploding gradient problems [1]}[2]}.
In short, RNNs exhibit the tendency to overwrite their internal memory thus losing the ability to remember useful information from past data timesteps. On the other hand, Long Short-Term Memory (LSTM) networks are a kind of RNN which deals with the aforementioned problems with the use of a more complex internal architecture, that makes the network able to decide whether information at each timestep are going to be remembered or forgotten [3]}.
This enables LSTMs to have greater control over their internal memory updates in order to effectively infer dependencies among consecutive data timesteps thus providing better results.
The fact that Early-est historical data consist of sequences of parameters computed at consecutive minutes makes LSTMs suitable for our needs. Namely, our LSTM should be able to learn how to predict the accuracy of magnitude estimates at a given minute on the basis of earthquake parameters computed at previous minutes.
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